Word2Vec News Recommendation System Design and Implementation:Based on Attention Mechanism and Embedding Optimization
[Purpose/significance]This paper is to design a Word2Vec news recommendation system based on Attention mecha-nism and Embedding optimization,and improve the recommendation effectiveness through the performance enhancement of word vector computation.[Method/process]This paper focus on solving three key technical difficulties in improving the construction of a news rec-ommendation system for Word2Vec:①Building a Word2Vec model based on Attention mechanism and Embedding optimization,provi-ding a word vector computing neural network for the system;②Improving the availability of MongoDB and Redis databases,and enhan-cing the robustness of database architecture in distributed frameworks;③Building an intelligent monitoring and operation platform.[Re-sult/conclusion]Compared with Word2Vec,Word2Vec based on Attention mechanism and Embedding optimization significantly im-proves loss value and accuracy.Database layer optimization and intelligent monitoring and operation platform improve system reliability and stability.
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